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Accessible Unlicensed Requires Authentication Published by De Gruyter 2020

10. Evaluating single- and multi-headed neural architectures for time-series forecasting of healthcare expenditures

Shruti Kaushik, Abhinav Choudhury, Varun Dutt, Nataraj Dasgupta, Sayee Natarajan and Larry A. Pickett


Artificial neural networks (ANNs) are increasingly being used in the healthcare domain for time-series predictions. However, for multivariate time-series predictions in the healthcare domain, the use of multi-headed neural network architectures has been less explored in the literature. Multi-headed architectures work on the idea that each independent variable (input series) can be handled by a separate ANN model (head) and the output of each of the of these ANN models (heads) can be combined before a prediction is made about a dependent variable. In this paper, we present three multi-headed neural network architectures and compare them with the corresponding single-headed neural network architectures to predict patients’ weekly average expenditures on certain pain medications. A multi-headed multilayer perceptron (MLP) model, a multi-headed long short-term memory (LSTM) model and a multi-headed convolutional neural network (CNN) model were calibrated along with their single-headed counterparts to predict patients’ weekly average expenditures on medications. Results revealed that the multi-headed models outperformed the singleheaded models and the multi-headed LSTM model outperformed the multi-headed MLP and CNN models across both pain medications. We highlight the utility of developing multi-headed neural architectures for prediction of patient-related expenditures in the healthcare domain.

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